Summary: 1
Real-time Bayesian Anomaly Detection for Environmental Sensor Data
David J. Hill
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
Phone: (217) 333-1657, Fax: (217) 333-6968, E-Mail: djhill1@uiuc.edu
Barbara S. Minsker
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign
Phone: (217) 333-9017, Fax: (217) 333-6968, E-Mail: minsker@uiuc.edu
Eyal Amir
Department of Computer Science, University of Illinois at Urbana-Champaign
Phone: (217) 333-8756, Fax: (217) 265-6591, E-Mail: eyal@cs.uiuc.edu
Recent advances in sensor technology are facilitating the deployment of sensors into the
environment that can produce measurements at high spatial and/or temporal resolutions. Not only
can these data be used to better characterize systems for improved modeling, but they can also be
used to produce better understandings of the mechanisms of environmental processes. One such
use of these data is anomaly detection to identify data that deviate from historical patterns. These
anomalous data can be caused by sensor or data transmission errors or by infrequent system
behaviors that are often of interest to the scientific or public safety communities. Thus, anomaly
detection has many practical applications, such as data quality assurance and control (QA/QC),
where anomalous data are treated as data errors; focused data collection, where anomalous data